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Abstract

Timely and cost-effective analytics over "big data" has emerged as a key ingredient for success in many businesses, scientific and
engineering disciplines, and government endeavors. Web clicks, social media, scientific experiments, and datacenter monitoring are among data
sources that generate vast amounts of raw data every day. The need to convert this raw data into useful information has spawned considerable
innovation in systems for large-scale data analytics, especially over the last decade. This monograph covers the design principles and core
features of systems for analyzing very large datasets using massively-parallel computation and storage techniques on large clusters of nodes.
We first discuss how the requirements of data analytics have evolved since the early work on parallel database systems. We then describe some
of the major technological innovations that have each spawned a distinct category of systems for data analytics. Each unique system category
is described along a number of dimensions including data model and query interface, storage layer, execution engine, query optimization,
scheduling, resource management, and fault tolerance. We conclude with a summary of present trends in large-scale data analytics.

Massively Parallel Databases and MapReduce Systems

Timely and cost-effective analytics over "big data" has emerged as a key ingredient for success in many businesses, scientific and
engineering disciplines, and government endeavors. Web clicks, social media, scientific experiments, and datacenter monitoring are among
data sources that generate vast amounts of raw data every day. The need to convert this raw data into useful information has spawned
considerable innovation in systems for large-scale data analytics, especially over the last decade.

Massively Parallel Databases and MapReduce Systems addresses the design principles and core features of systems for analyzing very large
datasets using massively-parallel computation and storage techniques on large clusters of nodes. It first discusses how the requirements of
data analytics have evolved since the early work on parallel database systems. It then describes some of the major technological innovations
that have each spawned a distinct category of systems for data analytics. Each unique system category is described along a number of
dimensions including data model and query interface, storage layer, execution engine, query optimization, scheduling, resource management,
and fault tolerance. It concludes with a summary of present trends in large-scale data analytics.

Massively Parallel Databases and MapReduce Systems is an ideal reference for anyone with a research or professional interest in large-scale data analytics.